Adds simple reinforcement learning model to repo

Author: Anmol Jagetia

simple_rl.py

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+import gym
+import universe
+import random
+
+
+#reinforcement learning step
+def determine_turn(turn, observation_n, j, total_sum, prev_total_sum, reward_n):
+	#for every 15 iterations, sum the total observations, and take the average
+	#if lower than 0, change the direction
+	#if we go 15+ iterations and get a reward each step, we're doing something right
+	#thats when we turn
+	if(j >= 15):
+		if(total_sum/ j ) == 0:
+			turn = True
+		else:
+			turn = False
+
+		#reset vars
+		total_sum = 0
+		j = 0
+		prev_total_sum = total_sum
+		total_sum = 0
+
+	else:
+		turn = False
+	if(observation_n != None):
+		#increment counter and reward sum
+		j+=1
+		total_sum += reward_n
+	return(turn, j, total_sum, prev_total_sum)
+
+
+
+
+def main():
+
+	#init environment
+	env = gym.make('flashgames.DuskDrive-v0')
+	observation_n = env.reset()
+
+	#init variables
+	# num of game iterations
+	n = 0
+	j = 0
+	#sum of observations
+	total_sum = 0
+	prev_total_sum = 0
+	turn = False
+
+	#define our turns or keyboard actions
+	left = [('KeyEvent', 'ArrowUp', True) ,('KeyEvent', 'ArrowLeft', True), ('KeyEvent', 'ArrowRight', False)]
+	right = [('KeyEvent', 'ArrowUp', True) ,('KeyEvent', 'ArrowLeft', False), ('KeyEvent', 'ArrowRight', True)]
+	Forward = [('KeyEvent', 'ArrowUp', True) ,('KeyEvent', 'ArrowLeft', False), ('KeyEvent', 'ArrowRight', False)]
+
+
+	#main logic
+	while True:
+		#increment a counter for number of iterations
+		n+=1
+
+		#if at least one iteration is made, check if turn is needed
+		if(n > 1):
+			#if at least one iteration, check if a turn
+			if(observation_n[0] != None):
+				#store the reward in the previous score
+				prev_score = reward_n[0]
+
+				#should we turn?
+				if(turn):
+					#pick a random event
+					#where to turn? 
+					event = random.choice([left,right])
+					#perform an action
+					action_n = [event for ob in observation_n]
+					#set turn to false
+					turn = False
+
+		elif(~turn):
+			#if no turn is needed, go straight
+			action_n = [Forward for ob in observation_n]
+
+
+		#if there is an obseravtion, game has started, check if turn needed
+		if(observation_n[0] != None):
+			turn, j, total_sum, prev_total_sum = determine_turn(turn, observation_n[0], j, total_sum, prev_total_sum, reward_n[0])
+
+		#save new variables for each iteration
+		observation_n, reward_n, done_n, info = env.step(action_n)
+
+		env.render()
+
+if __name__ == '__main__':
+	main()